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TT-PINN: A Tensor-Compressed Neural PDE Solver for Edge Computing
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abstract
Physics-informed neural networks (PINNs) have been increasingly employed due to their capability of modeling complex physics systems. To achieve better expressiveness, increasingly larger network sizes are required in many problems. This has caused challenges when we need to train PINNs on edge devices with limited memory, computing and energy resources. To enable training PINNs on edge devices, this paper proposes an end-to-end compressed PINN based on Tensor-Train decomposition. In solving a Helmholtz equation, our proposed model significantly outperforms the original PINNs with few parameters and achieves satisfactory prediction with up to 15$\times$ overall parameter reduction.
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Cited by 1 Pith paper
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Experimental Demonstration of an Optical Neural PDE Solver via On-Chip PINN Training
This paper reports a hardware demo in which a 1x4 microring weight bank is trained with zeroth-order optimization to solve a 1D heat equation to 5e-3 error.
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